Sensitivity of the resting state hemodynamic response ...dmarinaz/philtransa_hrf_accepted.pdf ·...

Phil. Trans. R. Soc. A. doi:10.1098/not yet assigned

Sensitivity of the resting state hemodynamic

response function estimation to autonomic nervous

system fluctuations

Guo-Rong Wua,b, Daniele Marinazzob

a) Key Laboratory of Cognition and Personality, Faculty of Psychology, Southwest University, Chongqing 400715, China. b) Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Ghent 9000, Belgium.

Keywords: resting state, fMRI, hemodynamic response, point process, cardiac fluctuations

The hemodynamic response function (HRF) is a key component of the blood-oxygen-level dependent

(BOLD) signal, providing the mapping between neural activity and the signal measured with fMRI. Most

of the time the HRF is associated with task-based fMRI protocols, in which its onset is explicitly included

in the design matrix. On the other hand the HRF also mediates the relationship between spontaneous

neural activity and the BOLD signal in resting state protocols, in which no explicit stimulus is taken into

account. It has been shown that resting state brain dynamics can be characterized by looking at sparse

BOLD “events”, which can be retrieved by point process analysis. These events can be then used to

retrieve the HRF at rest. Crucially, cardiac activity can also induce changes in the BOLD signal, thus

affecting both the number of these events and the estimation of the hemodynamic response. In this study

we compare the resting state hemodynamic response retrieved by means of a point process analysis taking

the cardiac fluctuations into account. We find that the resting state HRF estimation is significantly

modulated in the brainstem and surrounding cortical areas. From the analysis of two high quality datasets

with different temporal and spatial resolution, and through the investigation of intersubject correlation, we

suggest that spontaneous point process response durations are associated with the mean inter-beat interval

and low frequency power of heart rate variability in the brainstem.

*Author for correspondence Guo-Rong Wu Key Laboratory of Cognition and Personality, Faculty of Psychology, Southwest University, Chongqing 400715, China [email protected]

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Phil. Trans. R. Soc. A.

Spontaneous fluctuations in the BOLD signal are correlated with local field potentials activity (1). Analyses

of the functional connectivity (FC) between spontaneous low-frequency BOLD signal fluctuations have

identified a number of large-scale intrinsic connectivity networks (ICNs). These ICNs are related to

sensory, motor, language, social-emotional, and cognitive functions, suggesting that spontaneous BOLD

fluctuations plays a fundamental role in encoding brain function. The patterns mentioned above are

consistent across different levels of consciousness ranging from wakefulness down to sleep and anesthesia

(2), and find correlates with other imaging modalities including EEG and MEG (3, 4).

Recent studies have revealed that these ICNs can be identified by transiently synchronised spontaneous

BOLD “events” in the distributed brain regions, which can for example be identified as peaks above the

threshold. A growing amount of evidence points to these spontaneous BOLD events govern the brain

dynamic at rest (5-7); these spontaneous BOLD events can be revealed by point processes analysis (PPA)

(6), the core idea of it being in this context to isolate events in the BOLD time series (for example peaks in

the standardized time series) and to look at their spatial and temporal distribution. Compared to static FC

maps constructed from correlations between the whole time series, the FC maps derived by PPA appear to

be similar but carry more information on the individual states characterizing brain dynamics (6, 8). Both

dynamic FC and PPA are dependent on the variance of BOLD signal, thus they are sensitive to the

contribution from non-neuronal fluctuation. A wide range of artifacts may induce changes in BOLD signal,

such as thermal noise, hardware limitations (9) and participant’s movements inside the scanner (10). In

addition, as the BOLD signal is a measurement of changes in blood flow, oxygenation, and volume (11),

these changes may be caused by neuronal activity through neurovascular coupling, or alternatively arise

from any other physiological process that affect blood oxygenation or volume (12). Accordingly, noise and

physiological fluctuations may contribute to the rich spatio-temporal information content of dynamic brain

activity and connectivity (13). To reduce confounds deriving from non-neural activity-related process,

plenty of advanced noise cleanup method have been proposed (14). Due to the lack of ground truth in

noise removal, most of them focus on improvements in temporal signal-to-noise ratio (tSNR) or

reproducibility of FC maps. Yet, no study has explored how and to what extent these confounds affect

sparse spontaneous events. As proposed in previous studies, the spontaneous BOLD point process events

in resting state fMRI are assumed to be induced by these spontaneous neural events. Then it would be

possible to retrieve the corresponding hemodynamic response function (HRF) of the spontaneous neural

event at rest (15, 16). Apart from the variation of amplitude in BOLD signal, additional temporal

characteristics of the hemodynamic response, not available from tSNR and FC maps such as time to peak,

could be revealed by statistical analysis of spontaneous point process HRF.

Unlike thermal noise, physiological fluctuations can introduce fluctuations in the fMRI signal that are

uncoupled from neural activity, and are among the most important confounds in BOLD signal change (17).

In fact, cardiac mechanisms include changes in cerebral blood flow / volume, and arterial pulsatility (18).

Respiration effects include changes in B0 and arterial CO2 partial pressure (19). Though cardiac and

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respiratory cycles have relatively high frequencies in contrast to the typical low-frequency (<0.1Hz) BOLD

fluctuations, aliasing of physiological components to lower frequency range will inevitably occur due to

lower sampling rate in BOLD fMRI than cardiac and respiratory cycles (20). Recent studies have shown

that these nuisance confounds can significantly alter FC maps of the intrinsic brain networks, such as the

default mode network (21-23). Nonetheless, ample evidence has been collected to support that resting state

FC does have a neuronal underpinning and cannot purely be the result of physiological noise. To date, it’s

still not clear to what extent the physiological confounds affect the hemodynamic response retrieved by

point process analysis, and more information on this would be helpful for understanding the physiological

foundation of functional coupling among brain regions (24).

A number of methods have been developed to reduce physiological confounds in the BOLD signal (21, 22,

25-27). Retrospective image space correction of physiological noise (RETROICOR) is one of the most

employed methods to correct the cardiac and respiratory quasi-periodic fluctuations (25). However, it only

filters cyclic effects aliased in the fMRI signal, while the physiology-related low-frequency fluctuations

remain in the data. The time-shifted respiratory volumes per unit time (RVT) and heart rate (HR) time

series were introduced to account for more variance in BOLD signal that the one induced by non-periodic

fluctuations arising from cardiac and respiratory process (21, 22). These physiological noise correction

models on BOLD have been well validated. In light of what said so far, we feel it’s important to examine

their influence on hemodynamic response retrieved from spontaneous point processes.

Unfortunately, the advanced technique to remove the physiological confounds may also remove

meaningful variance components reflecting activity in the autonomic nervous system (ANS). ANS activity

should be considered as theoretically meaningful information, especially when studying brain areas

involved in decision making, conflict resolution and the experience of emotion (28). A seed based static FC

analysis of posterior cingulate cortex (PCC) has shown that general ANS activity is significantly related to

spontaneous BOLD activity in default mode network (DMN) and task positive network (29). The sliding

window based dynamic FC analysis further reveals that heart rate variability (HRV) covaries with

temporal changes in dorsal anterior cingulate cortex and amygdala connectivity maps respectively (30).

These studies indicate that resting state BOLD activity also contain both physiology-related spontaneous

neuronal activity and non-neural fluctuations (12, 21, 31, 32). Therefore it is critical to differentiate BOLD

point process from ANS modulation and physiological noise confounding. HRV is a popular non-invasive

indicant for assessing the activity in ANS. An analysis reporting how spontaneous point process HRF co-

vary with HRV could therefore explore the nature of autonomic regulation on brain activity at rest.

In the present study we investigate to which extent the estimation of the spontaneous point process

hemodynamic response is affected by changes in physiological noise, rather than solely by central

processes such as neural or astrocytic control. The combination of RETROICOR, RVT and HR are

employed to deconvolve the physiological fluctuations influence. As HRV is estimated from cardiac

activity, we only explore physiological noise correction effect of the quasi-periodic and non-periodic

cardiac fluctuations. Then spontaneous point process HRF maps are retrieved from the residual BOLD

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signal. Quantitative analysis on HRF map affected by cardiac fluctuation is performed. Finally, correlation

analysis between HRV and spontaneous point process HRF is further explored.

Two different resting-state (rs) fMRI datasets are included in the current study. The first dataset is the

enhanced Nathan Kline Institute-Rockland Sample (NKI-RS), acquired from 3-Tesla Siemens scanners (33).

Here we focus on two different TRs (TR=0.645s, TE=30 ms, FA=60°, 3mm isotropic voxels, 900 volumes;

and TR=2.5s, TE=30 ms, FA=80°, 3mm isotropic voxels, 120 volumes) sequentially collected by multiband

(acceleration factor = 4) and conventional EPI sequence. Anatomical images were obtained using an

MPRAGE sequence with a resolution of 1 mm3 isotropic. Right-handed subjects in release 4 with complete

demographic information were employed in our analysis (n=67, 17 females, age: 12~85 with mean 50.6 and

SD 20.8 years). They were instructed to keep their eyes open and fixate a crosshair.

The 7-Tesla rs fMRI test-retest dataset used in this study has been publicly released by the Consortium for

Reliability and Reproducibility (CoRR) project (34). Twenty-two participants (10 females) were scanned

during two sessions spaced one week apart. The subjects were instructed to stay awake, keep eye open and

focus on a cross. Their age ranged from 21 to 30 years with mean 25.1 and SD 2.2, one left handed subject

was excluded, resulting in all right handed subjects). Each session includes two 1.5 mm isotropic whole-

brain resting state scans (TR=3.0s, TE=17 ms, FA=70°, 1.5 mm isotropic voxels, 300 volumes, GRAPPA

acceleration with iPAT factor of 3) and gradient echo field map. Structural images were acquired by 3D

MP2RAGE sequence with a resolution of 0.7 mm isotropic.

Physiological data (respiratory and cardiac traces) was simultaneously recoded for each rs-fMRI scan. The

original data in 7T dataset (5000 Hz) were down-sampled to 100 Hz. The data in NKI-RS dataset is

recorded at a sample rate of 62.5 Hz. Two cardiac fluctuation correction models were constructed to

account for components related to 1) cardiac phases (CP), 2) heart rate (HR). The respiration fluctuations

are also included to account for the physiological noise influences: 1) respiratory phases (RP) and the

interaction effects between CP and RP (InterCRP), 2) respiratory volume per unit time (RVT). Models for

cardiac and respiratory phases and their interaction effects were based on RETROICOR (25) and its

extension (35). Cardiac and respiratory response functions were employed to model heart rate and

respiratory volume per time onto physiological process of the fMRI time series (21, 22, 26, 27). For each

subject, a set of 20 physiological regressors (i.e. 4st-order Fourier expansion for RP, 3rd-order Fourier

expansion for CP, 2nd-order Fourier expansion for InterCRP, RVT and HR) was created using the Matlab

PhysIO toolbox (http://www.translationalneuromodeling.org/tnu-checkphysretroicor-toolbox/) for each

slice in each fMRI run. Cardiac fluctuation correction based on different combinations of these regressors

was studied to investigate the effect of cardiac pulse, performing by a general linear model (GLM). The

combinations are:

1) RP & RVT (RPV-model),

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2) RP & RVT & CP & InterCRP (RPVC-model),

3) RP & RVT & HR (RPVH-model),

4) RP & RVT & CP & InterCRP & HR, i.e., all models (RPVCH-model).

Heart rate variability (HRV) analysis was performed on the inter-beat-interval (IBI) time series in each

resting state session scan, using the HRV analysis software (HRVAS,

https://github.com/jramshur/HRVAS). The IBI time series were calculated as the peak-to-peak interval

of photoplethysmography (PPG) signal. IBI outliers in each session were removed. The outliers were

defined as intervals deviating 20% from the previous interval. To alleviate any non-stationarities within IBI

time series, wavelet packet detrending was used before HRV analysis. Finally, time domain and

frequency-domain measures were derived from IBI series, included: mean IBI; the standard deviation of

the NN (normal-to-normal) interval series, SDNN; the root mean square of successive differences of the IBI

series, RMSSD; spectral power of low frequency (LF, 0.04~0.15HZ) and high frequency (HF, 0.15~0.4HZ)

band power and LF/HF ratio, which represents a measure of sympatho-vagal balance.

All structural images in both datasets were manually reoriented to the anterior commissure and

segmented into grey matter, white matter, and cerebrospinal fluid, using the standard segmentation option

in SPM 12 (36). Resting-state fMRI data pre-processing was subsequently carried out using both AFNI and

SPM12 package with default parameters (36, 37), including: slice timing correction (T), registration (R),

physiological noise model correction (C), despiking (D) and normalization (N). To examine the pre-

processing procedure effect on point process acquisition, three commonly used orders of pre-processing

steps were applied to the dataset: (1) DCTRN, (2) DRCTN, and (3) DTRCN. The raw volumes were

despiked using AFNI’s 3dDespike algorithm to mitigate the impact of outliers. In slice timing step, the EPI

volumes of each run were corrected for the temporal difference in acquisition among different slices to

match the middle time slice or half TR (for TR=0.645s); in the registration step, the images were realigned

to the first volume of the first run, the gradient echo field map was processed to create a voxel

displacement map and used to correct the realigned images for geometric distortion (only for 7T dataset),

then the generated mean image across all realigned volumes was coregistered with the structural image,

and the resulting warps applied to all the realigned volumes; in physiological noise correction step,

physiological noise models were regressed as the covariates, the physiological model regressors from

middle time slice was used for DTRCN, while each slice data were regressed by physiological model

regressors constructed from different time acquisition for DCTRN and DRCTN. Finally all the processed

BOLD images were spatially normalized into MNI space. A conjunction mask was then created to

sufficiently cover for all participants in each datasets.

Six head motion parameters obtained in the realigning step, Legendre polynomials orders up to 2nd were

included in a linear regression to remove possible spurious variances from the data. Then the residual time

series were temporally band-pass filtered (0.008-0.1Hz) and submitted for further HRF retrieval and

statistical analysis, including the standard deviation (SD) and coefficient of variation (CV, i.e. SD/Mean).

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We employ a blind deconvolution technique to retrieve spontaneous point process hemodynamic

response function (HRF) from resting-state BOLD-fMRI signal (15). A linear time-invariant model for the

observed resting state BOLD response is assumed. We hypothesize that a common HRF is shared across

the various spontaneous point process events at a given voxel, resulting in a more robust estimation. After

cardiac fluctuation correction, the BOLD signal )(ty at a particular voxel is given by:

)()()()( tcthtxty , (1)

where )(tx is a sum of time-shifted delta functions centered at the onset of each spontaneous point

process event and )(th is the hemodynamic response to these events, c is a constant term indicating the

baseline magnitude of the BOLD response, )(t represents additive noise and denotes convolution.

The noise errors are not independent in time due to aliased biorhythms and unmodelled neural activity,

and are accounted for using an AR(p) model during the parameter estimation (we set p=1 in current

study). Although no explicit external inputs exist in resting-state fMRI acquisitions, we still could retrieve

the timing of these spontaneous events by means of the blind deconvolution technique (15). The lag

between the peak of neural activation and the peak of BOLD response is presumed to be N

TRk seconds

(where TR

NPSTk 0 , N=3, PST is the peristimulus time, in the resting state sense, where the

“stimulus” is the neural event resulting in a BOLD signature). The timing set of these resting-state

BOLD transients is defined as the time points exceeding a given threshold around a local peak, is built in

the following way: )()(&)()(&)(,}{ iiiiii tytytytytytiS , where we set 2,1 and

(i.e. the SD) in the current study. The exact time lag can be obtained by minimizing the mean

squared error of equation (1), i.e. solving the optimization problem:

kSttxkSttxcthtxtykhkh

,0)(ˆ;,1)(ˆ,)()(ˆ)(argminˆ,ˆ2

,

(2)

In order to avoid pseudo point process events induced by motion artifacts, a temporal mask with

framewise displacement (FD)<0.3 was added to exclude these bad pseudo-event onsets from timing set S

by means of data scrubbing (38). A smoothed finite impulse response (sFIR) model is employed to retrieve

the spontaneous point process HRF shape (39).

To characterize the shape of the hemodynamic response, three parameters, namely response height

(including no normalized and normalized by baseline magnitude c, i.e. percent signal change, hereafter we

refer to as response height, and response height-PSC), time to peak, Full Width at Half Maximum

(FWHM), were estimated, which could be interpretable in terms of potential measures for response

magnitude, latency and duration of neuronal activity (40).

S

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After we retrieved the resting state HRF for each cardiac fluctuation correction model, response

height (-PSC) outlier were rejected by Grubbs test, then the corresponding HRF parameters for each

subject were spatially smoothed (8 mm FWHM), finally individually entered into a random-effects

analysis (one-way ANOVA within subjects, with three covariates (age, gender and mean FD) to identify

regions which showed significant hemodynamic differences after cardiac fluctuation correction, subjects

with mean FD>0.3 were excluded in the statistical analysis). Correlation between each HRV indicators and

HRF parameters were analyzed by multiple regressions with three covariates (age, gender, and mean FD).

Type I error due to multiple comparisons across voxels was controlled by familywise error rate (FWE,

voxel-wise correction, p<0.05, cluster size 20).

Four regressors of physiological noise correction models are entered into mass univariate GLM

analysis, and the adjusted R-square of cardiac fluctuations are estimated by nested model. To include head

motion parameters (obtained in the realigning step), here we only report the results after DTRCN

preprocessing. Figure 1 shows the averaged fraction of variance explained by quasi-periodic and non-

periodic cardiac fluctuations regressors at voxel level over subjects. Most of higher adjusted R-square

values for quasi periodic cardiac fluctuation are distributed on the brainstem (the anatomical locations

were obtained using the maximum probability tissue atlas from the OASIS-project (http://www.oasis-

brains.org/) included in SPM12 and provided by Neuromorphometrics, Inc. under academic

subscription (http://neuromorphometrics.com/)). For HR, the adjusted R-square values distribution is

much lower and more homogeneous, and higher explained variance can also be found in cortical

networks, such as DMN.

Figure 1. Spatial distribution of voxelwise adjusted R-squared values for different cardiac fluctuations in different TRs. 1st column: R2adj(MP+ RPVCH) - R2adj(MP+ RPVCH). 2nd column: R2adj(MP+ RPVCH) - R2adj(MP+ RPVH). 3rd column: R2adj(MP+ RPVCH) - R2adj(MP+ RPV). (MP: motion parameter).

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HRF parameters of each voxel are estimated and mapped on a brain template (Figure 2). The median

maps of each HRF parameters exhibit spatial heterogeneity across different physiological noise correction

models (Figure 3). They present similar spatial distributions: higher response height/FWHM/time to peak

is present in the occipital/frontal lobe and precuneus, higher response height-PSC is distributed in the

brainstem and surrounding areas. The baseline amplitudes in different MRI scanners exhibit different

spatial distributions. The interested reader can find spatial maps of these parameters in other datasets (16).

Figure 2. Median maps of HRF parameters (1st, 4th, 6th,7th columns) and BOLD CV/SD/Mean (2nd, 3rd,5th columns) across subjects (preprocessed by DRCTN).

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Figure 3. Main effect on HRF parameters of four cardiac fluctuations correction models with different preprocessing procedures (repeated measures ANOVA F-value, p<0.05, FWE correction). CTR: DCTRN; RCT: DRCTN; TRC: DTRCN.

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Repeated measures ANOVA reveal that HRF response height is significantly different across models.

The main effect of the cardiac fluctuations correction model on response height is mainly located in the

brainstem and the surrounding pulsatile CSF regions and cortex (Figure 3. p<0.05, FWE corrected). The

results are strongly affected by different preprocessing produces and magnetic field strengths. The post-

hoc analyses suggest that HR gives a limited contribution to the variance of the point process response

height (and PSC), while the significant magnitude increase caused by cardiac cycle. The main effect maps

of response height show highly similar spatial distribution with CV, SD and the response height-PSC (SD

and PSC are not shown in Figure 3). The other HRF parameters are less sensitive to cardiac fluctuation

correction. The cardiac fluctuation correction on FWHM exhibit high sensitive to different pre-processing

procedures. While the post-hoc analyses further indicate that cardiac cycle extend the response duration.

The only significant differences found in time to peak is in the 7T dataset (TR=3s); the post-Hoc tests show

quasi-periodic cardiac fluctuation extends the time latency in precuneus.

Only the correlation maps who passed the conjunction analysis obtained from two or three

preprocessing procedures are reported. The correlation map reveals that the results appear to be

dependent on the TR (Figure 4. p<0.05, FWE corrected). For TR=0.645s FWHM appears to be the only HRF

parameter significantly linearly correlated with two HRV indicators. One is the mean IBI in midbrain, pons

and surrounding areas: culmen, parahippocampal gyrus, thalamus, insula, superior temporal gyrus, and

dorsal anterior cingulate; another one is LF power in midbrain and cerebellum anterior lobe (Figure 4,

Top). These positive correlations are also significant without cardiac fluctuations correction in all

preprocessing procedures. For TR=2.5s only the response height and PSC are significantly correlated with

some HRV indicators: LF power, and SDNN (Figure 4, Middle and Bottom). The positive linear

relationship with LF power/SDNN in response magnitude map is mainly distributed in mid cingulate

cortex (MCC). More regions are found to be also correlated between LF power and response magnitude-

PSC, namely: cuneus, precuneus, inferior parietal lobule, angular, precentral gyrus, anterior cingulate

cortex (ACC), medial/superior frontal gyrus, and superior parietal lobule. The positive correlation

between response magnitudes-PSC and SDNN show a spatial pattern similar to the one of LF power, apart

from above reported regions, but extent to include hippocampus, parahippocampal gyrus, caudate,

middle/inferior/superior temporal gyrus, supramarginal gyrus, postcentral gyrus and inferior /middle

frontal.

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Figure 4. Correlation maps between HRV and HRF parameters (p<0.05, FWE correction). Top: TR=0.645s. Middle and Bottom: TR=2.5s (SDNN, LF). The warm (cool) color denotes positive (negative) T-value.

We investigated how cardiac fluctuations affect the resting state point process hemodynamic response

estimation. These quasi-periodic fluctuations appear to influence the point process HRF magnitude and

duration mainly in the brainstem and surrounding cortical. In addition, our results suggest that HRF

parameters of are sensitive to preprocessing procedures. However, we can found a robust correlation

between spontaneous point process response duration and mean interbeat interval (higher IBI correspond

to slower heart rate) /low frequency power in brainstem at short TR dataset (TR=0.645). Such positive

correlations are persistent and not affect by cardiac fluctuation correction.

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Previous neuroimaging studies aimed to elucidate brain-heart interactions have looked at the regional

cerebral blood flow, derived from PET or ASL (31, 41), at brain activity in PET/task fMRI or brain

connectivity in resting state fMRI (12, 22, 31). Our results show for the first time that the reconstruction of

hemodynamic response at rest is affected by cardiac activity confounds, especially affecting the temporal

information on the duration and latency of the BOLD signature of cortical events. Our findings are

consistent with the PET and fMRI studies above mentioned, involving the brainstem and surrounding

areas, insula and dorsal anterior cingulate. The brainstem, including the medulla oblongata, pons and

midbrain, is the most important integrative control center for autonomic nervous system function and

plays an important role in the regulation of cardiac and respiratory function (42, 43). There are more

potential sources of signal variance in the brainstem than in any other part of the brain, due to its

anatomical structure: it’s highly vascularized with arteries and veins in midbrain, is surrounded by the

pulsatile flow of the CSF, and it’s more connected to the lungs. These factors have been reported to induce

stronger changes in the magnetic field B0 (44, 45). The well-established physiological noise correction

methods sharply regress out a very large proportion of spurious variation in the brainstem signal.

Nonetheless, the linear correlation analysis across subjects still shows that brainstem activity is associated

with HRV. The simple mean value of heart rate reveals such relationship with spontaneous point process

response durations. Also LF power, which is generally thought to be modulated by both sympathetic and

parasympathetic activity, is robustly correlated with the response duration in the midbrain. These

phenomena are not affected by different processing pipelines, but cannot be evidenced with longer TRs.

This may be explained by the fact that a more precise estimation of hemodynamic response duration

requires a higher sample rate. To further confirm the effect from different magnitude field strength, a short

TR acquisition with a 7T MRI scanner would be a great resource.

With longer TRs the associations between HRF parameters and HRV are more sensitive to the

processing steps. No significant correlation could be found after performing the physiological noise

correction first. Moreover, the spontaneous point process response magnitude and its normalization (PSC)

are the only indexes that are correlated with HRV parameters. Apart from LF power, SDNN and RMSSD

are also significantly correlated with brain areas involved in autonomic activity. The former also reflects

both sympathetic and parasympathetic activity, providing an index of total HRV (46). The latter estimates

the short-term components of HRV, allowing an estimate of vagal nerve activity (47). Our results reveal

that LF and SDNN share regions in MCC that are correlated with response magnitude and its

normalization. They are canonical brain areas associated with sympathetic regulation (43). Other regions

showing significant associations in the current analysis have been reported to be related to autonomic

activity in previous studies (43). In fact, the insular cortex is posited to act as an integrator on the brain-

heart axis (48): it has a prominent role in limbic-autonomic integration and is involved in the perception of

emotional significance (49); it also participates in visceral motor regulation, including blood pressure

control, in cooperation with subcortical autonomic centers (50-52). The dorsal anterior cingulate cortex

(dACC) is also involved in autonomic control (30, 53); the network consisting of insula, dACC, and

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amygdala has been described as crucial in the regulation of central autonomic nervous system (54). A

human neuroimaging meta-analysis on electrodermal activity and high-frequency HRV revealed that

midbrain, insula and supramarginal gyrus are associated with sympathetic and parasympathetic

regulation; ACC, thalamus, and primary somatosensory cortex are associated to sympathetic regulation,

while precuneus, superior temporal gyri and angular are associated to parasympathetic regulations (43).

The precuneus and angular gyrus are also among the key nodes of DMN. Several studies have shown that

FC maps of the DMN are modulated by heart rate and RVT (12, 22). Intriguingly, the spatial extent of the

correlation map with normalized response magnitude is larger than when raw response magnitude is

considered. This phenomenon is better observed when magnetic field strength increases. It is worth

mentioning that there is no significant correlation between HRF estimation and HRV after conjunction

analysis in the 7T dataset. Apart from stringent thresholds for significance, magnetic field strength and age

distributions are different in the two datasets: a study has shown that SDNN index exhibits a linearly

correlated pattern of decline with aging for both genders (47).

A recent study reported a significantly decreased test-retest reliability in FC by physiological noise

correction techniques (23). These results were explained by assuming that these physiological fluctuations

are similar and reproducible within a subject across sessions, but to a lesser extent than between subjects.

Another explanation given in the same study posited that physiological noise correction could also remove

the signal of interest.

Physiological fluctuations have been shown to be proportional to magnetic field strength (55): the

physiological processes may therefore contribute much more to variance in BOLD signal when data are

acquired with a strong field. Apart from cardiac fluctuations, respiration is another physiological

fluctuation that has also been found to strongly modulate the resting state fMRI BOLD signal (26, 27).

Respiration fluctuations will induce variations in arterial level of CO2, then cause either validations or

vasoconstriction, resulting in blood flow and oxygenation changes (56). HRF magnitude variation is

intrinsically related to the CO2 centration due to vascular reactivity (19, 57, 58). In the current study, a

vascular modulator such as a breath-holding task was not present in all datasets; RVT is used as a

surrogate for arterial CO2 concentration to capture breathing rate and depth from respiratory belt

measurements suggested by (26). In addition, as respiration and cardiac pulsations are tightly correlated

(59, 60), we firstly partial out respiratory fluctuations before investigating the impact of cardiac

fluctuations on the estimation of resting state point process HRF. The spontaneous events retrieved by

point process may include actual neural events, autonomic activities and their interactions. However, in

the estimation of the HRF, we hypothesize that they result in a common shape. This may intrinsically limit

the disambiguation of the two.

To reduce the computational cost and the bias in the linear estimation framework, we employ

canonical functions for HR and RVT hemodynamic response (22, 27). Moreover, the lagged RVT and HR

regressors are not included in our analysis. These may reduce the contribution of HR in regression model.

However, increasing the number of regressors may induce more bias in GLM model especially for short

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time series (120 volumes in TR=2.5). The more flexible sFIR model could then minimize the risk of

assumptions about the spontaneous point process HRF shape (39). Also, the sFIR model may also include

the components related to cardiac fluctuation in the hemodynamic response, when the latter is not

eliminated from the BOLD signal. It has been shown that different processing steps could dramatically

change the tSNR in fMRI BOLD signal. In particular, volume registration before physiological noise

correction and not performing slice timing correction before physiological noise correction will result in

the greatest reduction of temporal noise (61). Such effect on estimation of spontaneous point process HRF

has not been investigated. Resting state point process is dependent on the variance of BOLD signal;

repeated measures ANOVA results show that HRF magnitudes are similar to BOLD CV and SD in most

cases. Our results confirm that different processing steps affect the HRF estimation, not only of its

magnitude but also of its temporal parameters (latency and duration). In addition, we find that the effects

of HR on HRF estimation are more evident when the DTRCN processing procedure is used together with

3T dataset, and when DRCTN is used with short TR (0.645s). Apart from the differences due to processing,

sFIR model is essentially more sensitive to temporal noise (40).

The present study has some limitations that should be noted. First of all, the proposed method to

retrieve the HRF at rest only uses BOLD data. In (16) we have started to explore some validation strategies

involving models, ASL, PET and simultaneous EEG data. The most convincing validation would

nonetheless involve data where the neural activity and the BOLD signal are both extracted, for instance an

experiment with simultaneous BOLD signal and intracortical recordings of neural signals (62). It’s worth to

note that the processing order “DTRCN” may not capture the aliased physiological perturbation due to the

placement of RETROICOR after slice-timing correction, especially for long TR datasets. Performing

despiking before physiological noise correction could improve the regression model fitting by removing

large spikes. The despiking procedure was skipped in (38, 61); nonetheless it appears to improve the

results of volume registration over time as illustrated in (63). However, each procedure has potential

disadvantages: temporal correction (slice-timing correction and despking) before realignment may

interpolate signals from different brain regions if there is significant head movement; temporal correction

after realignment on the other hand may shift voxels to adjacent slices and hence disturb temporal order:

this problem is especially relevant for interleaved and multiband acquisitions such as those used in the

current study. To cope with the latter problem, motion-modified RETROICOR has been proposed to

include slice contribution to every voxel (61). This procedure however might induce higher computational

costs and a bias in the regression model. Therefore, the influences of different preprocessing procedures on

HRF estimation should be further explored. In addition, we did not find any significant association

between amygdala with HRV parameters. Moreover, no significant correlation with HF power or LF/LF

was found after conjunction analysis.

It’s well known that head motion is an unavoidable source of noise in the BOLD signal (10). To avoid

motion-related artifacts contribution to point process detection, in addition to adding motion parameters

as a nuisance regressor in the GLM, data scrubbing was performed (38), and Mean FD of each subject was

15


included as a covariate for further statistical analysis (64). This procedure ensures that our findings are

unlikely affected by motion artifact.

This study has demonstrated the impact of physiological noise correction on resting state HRF

estimation, validated at different TRs and magnetic field strength. Several processing pipelines are

employed to explore the sensitivity in estimation of resting state HRF. Intersubject correlation analyses

between HRF and HRV parameters suggest that autonomic nervous system fluctuations modulate the

estimation of spontaneous point process response in brainstem.

Information on the following should be included wherever relevant. Funding Statement G.R.W. was supported by the Natural Science Foundation of China (Grant No. 61403312), and the

Fundamental Research Funds for the Central Universities (Grant No. 2362014xk04).

Data Accessibility NKI Rockland data is available and described at http://dx.doi.org/10.3389/fnins.2012.00152 CoRR 7T TRT data is available and described at http://dx.doi.org/ 10.1038/sdata.2014.54

Competing Interests We have no competing interests.

G.R.W. and D. M. designed the research; G.R.W. analysed the data; G.R.W. and D.M. wrote the paper.

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